Time Series Forecasting with Machine Learning

Thank you for this video!

How to build ARIMA models in Python without dates? If I'm estimating a target boats sinusoidal position in the ocean, do I wanna map milliseconds as dates 🤔, nah

This was awesome!

You are awesome!! Thank you for this video

Do you have project with multivariate analysis?

One note: you can actually use the machine learning model (the non-traditional model as you call it in the video) to dynamically predict whatever number of future points you want, you just have to implement recursion manually. Train the model to predict one step ahead, then use that prediction to predict 2nd step and so forth. This will very likely become "hard to get right" similarly to what you said about traditional models, as it's a much more complicated problem, but it is doable.

nice overview. thanks.

Thanks for the awesome comparison! Very insightful!

Great explanation, thanks

i need a help from you regarding time series ....how can i contact u

please how can i contact you

Simple explanation and very good

wow. you explain concepts very well

If you add regressors to Prophet, doest that also make it multi variant?

Very simplified. Thank you.

I loved this video. Such great information easily explained. Thankyou

Great

Super helpful! Thank you so much!

Don't stop making videos. You have a great teaching video.

Thank you for the useful explanations!

This video is great! I loved how you have put down the different methods so clearly and their pros and cons.

Cheers to more videos!! :)

Thanks a lot, very useful for me. I was wondering whether we can use time series forecasting using regression trees or not?

very well explined.. the issue was also looked at the business front as well.. were as the traditional IT gig would explain in the point unlike this..
it shows the understing of the business is important to adapt to these new tecnologies..

thanks. very informative!

annoying super American voice

SARIMAX is multivariate

Loved this video...!
Thankyouuuuuu

So precise!!! THanks

Danke je wel!

I worked hard on forex dataseries:
EURUSD tick resolution, compressed with wavelets, passed into LSTM under keras.
Got 73% accuracy on the next minute: not bad for experimental results?
What gives me headaches:
- do I always need to make the timeseries *stationary*?
- How to scale perfectly my timeseries, according to what model im going to use (lstm, mlp, sklearn regressor...)?
- Do I have to use stateless or stateful lstm???
- Does it have soem sense to shuffle sequenses before training lstm?
I could not find clear answer anywhere on the net...

I was looking for an introduction to time series forecasting for a personal project (gas price prediction, since gas prices here in Germany are kinda high), and this was the perfect primer for time-series forecasting. Not too dumbed down, and not too complicated. And, obviously a GREAT example.

I'm trying predictive sequence or whatever it's called, from 0 knowledge in programming. Will prophet be a good starting point to get my feet wet?

Well done!

Can we add categorical variables as explanatory variables as well or the variables should be time variant?

I'm sure you're aware of certain psychological phenomena & "fringe theories" emerging on the surface? Some people, while not versed in the particulars, nontheless are beginning to trace the origins of said phenomena to, vectors adjacent, or even linked with our area of this continuum.

I'm trying not to use any words that would attract scrutiny but that's becoming difficult. That narcissistic zealot, blabbing about timelines & Lovecraft. who's ass we are encouraged to kiss ROWS G.D QuÆv.

I know people are getting the same threats I am...Either someone has worked out the Quasi-cosmic code, they've copied the artefact, or the אºD's are angry.

Maybe we should focus on what's important. BTW I ASSUME NO-ONES SEEING DARK RECURRING NUMBERS & SEEING DEEP LEARNING OBSERVER NOISE? ME NEITHER!

Hi, great video! I'm just getting into time series forecasting, and you teached me a lot, thank you :)
Could you make a video about Graph Attention Networks for time series forecasting?

Thank you so much for your nice video. -- From Bangladesh

I just want to say how much I love that it’s my grandma that has a laptop repair shop. 😍

Thank you for this awesome video! I'm pretty new to ML and time series and this is so helpful and clear. I'm actually working on an assessment for a Data Analyst role that I'm interviewing for and I'm tasked with forecasting travel bookings. Glad I came across your video and excited to check out your other ones!

You can make up any term and I wouldn’t know if it was real or fake. Dog apostle maxer.

thank you

This is a seriously great introduction!

This is still rather opaque..for certain classes of machine learning models it seems like the data assumption is i.i.d correct? Such that datapoints farther in the time horizon will be treated exactly the same as datapoints closer to the time horizon; maybe I am not understanding.

Why can't we extend the machine learning model which predicts for next day to the 3days or 10days by simply using 1 feature which keeps track of the previous day? I can't see why can't we extend.

Great video! What is it that makes Prophet NOT a machine learning model? I would have thought all the models that learn from the past can be classified as machine learning.

Please correct if wrong: Traditional time series models are not necessarily recursive: MA, IMA models are not regressive

What about linear regression moving average and autoregression and Taylor series methods ? Why they weren't discussed

Well I know Reinforcement learning is being used to model financial time series control aka the stock market but can we track back and make a simple forecasting model with Reinforcement learning in which the actions dont really have nothing to do other than predictions

Hey, nice work. For the next video, can you implement a Temporal convolutional network for time series forecasting(Load Forecasting)?